In order to obtain more precise poses of the queries, additional relative pose estimation with respect to the retrieved images is required. Traditionally, relative pose estimation is tackled by epipolar geometry, relying on the 2D-2D correspondences determined by local descriptors [1]}, [2]}. In contrast, deep learning approaches regress the relative poses straightforwardly from pairwise images. For example, NN-Net [3]} utilized neural network to estimate the pairwise relative poses between the query and the top N ranked references. A triangulation-based fusion algorithm coalesces the predicted N relative poses and the ground truth of 3D geometry poses, and the absolute query pose can be naturally calculated. Furthermore, Relocnet [4]} introduces a frustum overlap loss to assist global descriptors learning that are suitable for camera localization. Motivated by these, CamNet [5]} applies two stages retrieval, image-based coarse retrieval and pose-based fine retrieval, to select the most similar reference frames for finally precise pose estimation. Without the need of training on specific scenarios, reference-based approaches are naturally scalable and flexible to be utilized in new scenarios.
Since reference-based methods need to maintain a database of geo-tagged images, they are more trivial to scale to large-scale scenarios, compared with the structure-based counterparts. Overall, these image retrieval based methods achieve a trade-off between accuracy and scalability.
